contrib.training.train

tf.contrib.training.train

tf.contrib.training.train

train(
    train_op,
    logdir,
    master='',
    is_chief=True,
    scaffold=None,
    hooks=None,
    chief_only_hooks=None,
    save_checkpoint_secs=600,
    save_summaries_steps=100,
    config=None
)

Defined in tensorflow/contrib/training/python/training/training.py.

Runs the training loop.

Args:

• train_op: A Tensor that, when executed, will apply the gradients and return the loss value.
• logdir: The directory where the graph and checkpoints are saved.
• master: The URL of the master.
• is_chief: Specifies whether or not the training is being run by the primary replica during replica training.
• scaffold: An tf.train.Scaffold instance.
• hooks: List of tf.train.SessionRunHook callbacks which are run inside the training loop.
• chief_only_hooks: List of tf.train.SessionRunHook instances which are run inside the training loop for the chief trainer only.
• save_checkpoint_secs: The frequency, in seconds, that a checkpoint is saved using a default checkpoint saver. If save_checkpoint_secs is set to None, then the default checkpoint saver isn't used.
• save_summaries_steps: The frequency, in number of global steps, that the summaries are written to disk using a default summary saver. If save_summaries_steps is set to None, then the default summary saver isn't used.
• config: An instance of tf.ConfigProto.

Returns:

the value of the loss function after training.

Raises:

• ValueError: if logdir is None and either save_checkpoint_secs or save_summaries_steps are `None.